Systems, methods, and storage media for evaluating images

ABSTRACT

Embodiments may: select a set of training images; extract a first set of features from each training image of the set of training images to generate a first feature tensor for each training image; extract a second set of features from each training image to generate a second feature tensor for each training image; reduce a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image; reduce a dimensionality of each second feature tensor to generate a second modified feature tensor for each training image; construct a first generative model representing the first set of features and a second generative model representing the second set of features of the set of training images; identify a first candidate image; and apply a regression algorithm to the first candidate image and each of the first generative model and the second generative model to determine whether the first candidate image is similar to the set of training images.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority as a continuation to U.S. patentapplication Ser. No. 16/672,227, titled “SYSTEMS, METHODS, AND STORAGEMEDIA FOR EVALUATING IMAGES” and filed on Nov. 1, 2019, which claimspriority as a divisional to U.S. patent application Ser. No. 16/271,780,titled “SYSTEMS, METHODS, AND STORAGE MEDIA FOR EVALUATING IMAGES” andfiled on Feb. 8, 2019, each of which is hereby incorporated by referencein its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems, methods, and storage mediafor evaluating images.

BACKGROUND

Many people use the internet every day. Some use it to discoverinformation such as news, recipes, phone numbers, etc. Some use theinternet to communicate with others through mediums such as chat rooms,message boards, and e-mail. Traffic on the internet is large and manypeople use the internet for extended amounts of time.

Users of the internet may also use the internet to such a degree thatadvertisers can effectively market goods and services to customers orpotential customers using the internet. For example, a host oradministrator of a website may place advertisements on popular pages oftheir website. Such advertisements may be related to other parts of thewebsite or goods that can be purchased that are related to the website.In another example, such advertisements can be unrelated to the website.For example, the website host or administrator may sell space toadvertise on and within the website to third parties, much like abillboard might sell or lease ad space to third parties who would likepassersby to see the advertisement.

SUMMARY

One aspect of the present disclosure relates to a system configured forevaluating images. The system may include one or more hardwareprocessors configured by machine-readable instructions. The processor(s)may be configured to select a set of training images. The processor(s)may be configured to extract a first set of features from each trainingimage of the set of training images to generate a first feature tensorfor each training image. The processor(s) may be configured to extract asecond set of features from each training image to generate a secondfeature tensor for each training image. The processor(s) may beconfigured to reduce a dimensionality of each first feature tensor togenerate a first modified feature tensor for each training image. Theprocessor(s) may be configured to reduce a dimensionality of each secondfeature tensor to generate a second modified feature tensor for eachtraining image. The processor(s) may be configured to construct a firstgenerative model representing the first set of features and a secondgenerative model representing the second set of features of the set oftraining images, based on the first modified feature tensors and thesecond modified feature tensors of each training image of the set oftraining images. The processor(s) may be configured to identify a firstcandidate image. The processor(s) may be configured to apply aregression algorithm to the first candidate image and each of the firstgenerative model and the second generative model to determine whetherthe first candidate image is similar to the set of training images.

In some implementations of the system, the processor(s) may beconfigured to calculate a similarity score representing a degree ofvisual similarity between the first candidate image and the set oftraining images, based on the regression algorithm.

In some implementations of the system, the processor(s) may beconfigured to calculate a uniqueness score of the first candidate imagewith respect to the set of training images.

In some implementations of the system, calculating the uniqueness scoreof the first candidate image may include calculating an inverse of thesimilarity score. In some implementations of the system, calculating theuniqueness score of the first candidate image may include identifyingthe inverse as the uniqueness score.

In some implementations of the system, the processor(s) may beconfigured to extract features from the first candidate image togenerate a candidate image feature tensor. In some implementations ofthe system, the features may correspond to the first set of featuresextracted from each candidate image. In some implementations of thesystem, the processor(s) may be configured to reduce a dimensionality ofthe candidate image feature tensor to generate a modified candidateimage feature tensor. In some implementations of the system, determiningwhether the candidate image may be similar to the set of training imagesincludes comparing the modified candidate image feature tensor with thefirst generative model.

In some implementations of the system, the processor(s) may beconfigured to apply a weight to the features extracted from thecandidate image to generate a set of weighted candidate image features.In some implementations of the system, the candidate image featuretensor may be generated based on the set of weighted candidate imagefeatures.

In some implementations of the system, the first set of featuresextracted from each training image may include object features. In someimplementations of the system, the processor(s) may be configured toextract the first set of features from each training image bypropagating data corresponding to each training image through at leastone network including at least one of an object detection neuralnetwork, an object classification neural network, or an objectrecognition neural network. In some implementations of the system, thenetwork may include an input layer, a plurality of intermediate layers,and an output layer. In some implementations of the system, theprocessor(s) may be configured to extract the first set of features fromeach training image by extracting outputs from at least one of theplurality of intermediate layers of the network.

In some implementations of the system, extracting the first set offeatures from each training image may include extracting at least one ofa set of object features, a set of scene features, a set of intensityfeatures, a set of contrast features, a set of color features, and a setof blurriness features from each training image.

In some implementations of the system, the processor(s) may beconfigured to identify respective locations of the first feature tensorand the second feature tensor in a feature space defined by the firstset of features and the second set of features. In some implementationsof the system, the processor(s) may be configured to generate a visualsignature for the set of training images based on the respectivelocations of the first feature tensor and the second feature tensor.

In some implementations of the system, the processor(s) may beconfigured to select the set of training images based on at least one ofa common author, a common origin, or a common theme.

In some implementations of the system, the processor(s) may beconfigured to identify a set of candidate images including the firstcandidate image. In some implementations of the system, the processor(s)may be configured to determine, for each candidate image of the set ofcandidate images, whether the candidate image is similar to the set oftraining images based on the first candidate image and each of the firstgenerative model and the second generative model. In someimplementations of the system, the processor(s) may be configured toidentify a subset of the set of candidate images that are similar to theset of training images.

In some implementations of the system, the processor(s) may beconfigured to provide a graphical user interface to be displayed on acomputing device. In some implementations of the system, the graphicaluser interface may display a plurality of indications corresponding tothe set of candidate images. In some implementations of the system, theprocessor(s) may be configured to receive a user selection of a firstindication or the plurality of indications corresponding to the firstcandidate image.

In some implementations of the system, the processor(s) may beconfigured to identify a brand attribute. In some implementations of thesystem, the processor(s) may be configured to select the first set offeatures to be extracted from the set of training images based at leastin part on the brand attribute.

In some implementations of the system, reducing the dimensionality ofeach first feature tensor may include applying principal componentanalysis to each first feature tensor to generate a first modifiedfeature tensor for each training image.

Another aspect of the present disclosure relates to a method forevaluating images. The method may include selecting a set of trainingimages. The method may include extracting a first set of features fromeach training image of the set of training images to generate a firstfeature tensor for each training image. The method may includeextracting a second set of features from each training image to generatea second feature tensor for each training image. The method may includereducing a dimensionality of each first feature tensor to generate afirst modified feature tensor for each training image. The method mayinclude reducing a dimensionality of each second feature tensor togenerate a second modified feature tensor for each training image. Themethod may include constructing a first generative model representingthe first set of features and a second generative model representing thesecond set of features of the set of training images, based on the firstmodified feature tensors and the second modified feature tensors of eachtraining image of the set of training images. The method may includeidentifying a first candidate image. The method may include applying aregression algorithm to the first candidate image and each of the firstgenerative model and the second generative model to determine whetherthe first candidate image is similar to the set of training images.

In some implementations of the method, it may include calculating asimilarity score representing a degree of visual similarity between thefirst candidate image and the set of training images, based on theregression algorithm.

In some implementations of the method, it may include calculating auniqueness score of the first candidate image with respect to the set oftraining images.

In some implementations of the method, calculating the uniqueness scoreof the first candidate image may include calculating an inverse of thesimilarity score. In some implementations of the method, calculating theuniqueness score of the first candidate image may include identifyingthe inverse as the uniqueness score.

In some implementations of the method, it may include extractingfeatures from the first candidate image to generate a candidate imagefeature tensor. In some implementations of the method, the features maycorrespond to the first set of features extracted from each candidateimage. In some implementations of the method, it may include reducing adimensionality of the candidate image feature tensor to generate amodified candidate image feature tensor. In some implementations of themethod, determining whether the candidate image may be similar to theset of training images includes comparing the modified candidate imagefeature tensor with the first generative model.

In some implementations of the method, it may include applying a weightto the features extracted from the candidate image to generate a set ofweighted candidate image features. In some implementations of themethod, the candidate image feature tensor may be generated based on theset of weighted candidate image features.

In some implementations of the method, the first set of featuresextracted from each training image may include object features. In someimplementations of the method, it may include extracting the first setof features from each training image by propagating data correspondingto each training image through at least one network including at leastone of an object detection neural network, an object classificationneural network, or an object recognition neural network. In someimplementations of the method, the network may include an input layer, aplurality of intermediate layers, and an output layer. In someimplementations of the method, it may include extracting the first setof features from each training image by extracting outputs from at leastone of the plurality of intermediate layers of the network.

In some implementations of the method, extracting the first set offeatures from each training image may include extracting at least one ofa set of object features, a set of scene features, a set of intensityfeatures, a set of contrast features, a set of color features, and a setof blurriness features from each training image.

In some implementations of the method, it may include identifyingrespective locations of the first feature tensor and the second featuretensor in a feature space defined by the first set of features and thesecond set of features. In some implementations of the method, it mayinclude generating a visual signature for the set of training imagesbased on the respective locations of the first feature tensor and thesecond feature tensor.

In some implementations of the method, it may include selecting the setof training images based on at least one of a common author, a commonorigin, or a common theme.

In some implementations of the method, it may include identifying a setof candidate images including the first candidate image. In someimplementations of the method, it may include determining, for eachcandidate image of the set of candidate images, whether the candidateimage is similar to the set of training images based on the firstcandidate image and each of the first generative model and the secondgenerative model. In some implementations of the method, it may includeidentifying a subset of the set of candidate images that are similar tothe set of training images.

In some implementations of the method, it may include providing agraphical user interface to be displayed on a computing device. In someimplementations of the method, the graphical user interface may displaya plurality of indications corresponding to the set of candidate images.In some implementations of the method, it may include receiving a userselection of a first indication or the plurality of indicationscorresponding to the first candidate image.

In some implementations of the method, it may include identifying abrand attribute. In some implementations of the method, it may includeselecting the first set of features to be extracted from the set oftraining images based at least in part on the brand attribute.

In some implementations of the method, reducing the dimensionality ofeach first feature tensor may include applying principal componentanalysis to each first feature tensor to generate a first modifiedfeature tensor for each training image.

Yet another aspect of the present disclosure relates to a non-transientcomputer-readable storage medium having instructions embodied thereon,the instructions being executable by one or more processors to perform amethod for evaluating images. The method may include selecting a set oftraining images. The method may include extracting a first set offeatures from each training image of the set of training images togenerate a first feature tensor for each training image. The method mayinclude extracting a second set of features from each training image togenerate a second feature tensor for each training image. The method mayinclude reducing a dimensionality of each first feature tensor togenerate a first modified feature tensor for each training image. Themethod may include reducing a dimensionality of each second featuretensor to generate a second modified feature tensor for each trainingimage. The method may include constructing a first generative modelrepresenting the first set of features and a second generative modelrepresenting the second set of features of the set of training images,based on the first modified feature tensors and the second modifiedfeature tensors of each training image of the set of training images.The method may include identifying a first candidate image. The methodmay include applying a regression algorithm to the first candidate imageand each of the first generative model and the second generative modelto determine whether the first candidate image is similar to the set oftraining images.

In some implementations of the computer-readable storage medium, themethod may include calculating a similarity score representing a degreeof visual similarity between the first candidate image and the set oftraining images, based on the regression algorithm.

In some implementations of the computer-readable storage medium, themethod may include calculating a uniqueness score of the first candidateimage with respect to the set of training images.

In some implementations of the computer-readable storage medium,calculating the uniqueness score of the first candidate image mayinclude calculating an inverse of the similarity score. In someimplementations of the computer-readable storage medium, calculating theuniqueness score of the first candidate image may include identifyingthe inverse as the uniqueness score.

In some implementations of the computer-readable storage medium, themethod may include extracting features from the first candidate image togenerate a candidate image feature tensor. In some implementations ofthe computer-readable storage medium, the features may correspond to thefirst set of features extracted from each candidate image. In someimplementations of the computer-readable storage medium, the method mayinclude reducing a dimensionality of the candidate image feature tensorto generate a modified candidate image feature tensor. In someimplementations of the computer-readable storage medium, determiningwhether the candidate image may be similar to the set of training imagesincludes comparing the modified candidate image feature tensor with thefirst generative model.

In some implementations of the computer-readable storage medium, themethod may include applying a weight to the features extracted from thecandidate image to generate a set of weighted candidate image features.In some implementations of the computer-readable storage medium, thecandidate image feature tensor may be generated based on the set ofweighted candidate image features.

In some implementations of the computer-readable storage medium, thefirst set of features extracted from each training image may includeobject features. In some implementations of the computer-readablestorage medium, the method may include extracting the first set offeatures from each training image by propagating data corresponding toeach training image through at least one network including at least oneof an object detection neural network, an object classification neuralnetwork, or an object recognition neural network. In someimplementations of the computer-readable storage medium, the network mayinclude an input layer, a plurality of intermediate layers, and anoutput layer. In some implementations of the computer-readable storagemedium, the method may include extracting the first set of features fromeach training image by extracting outputs from at least one of theplurality of intermediate layers of the network.

In some implementations of the computer-readable storage medium,extracting the first set of features from each training image mayinclude extracting at least one of a set of object features, a set ofscene features, a set of intensity features, a set of contrast features,a set of color features, and a set of blurriness features from eachtraining image.

In some implementations of the computer-readable storage medium, themethod may include identifying respective locations of the first featuretensor and the second feature tensor in a feature space defined by thefirst set of features and the second set of features. In someimplementations of the computer-readable storage medium, the method mayinclude generating a visual signature for the set of training imagesbased on the respective locations of the first feature tensor and thesecond feature tensor.

In some implementations of the computer-readable storage medium, themethod may include selecting the set of training images based on atleast one of a common author, a common origin, or a common theme.

In some implementations of the computer-readable storage medium, themethod may include identifying a set of candidate images including thefirst candidate image. In some implementations of the computer-readablestorage medium, the method may include determining, for each candidateimage of the set of candidate images, whether the candidate image issimilar to the set of training images based on the first candidate imageand each of the first generative model and the second generative model.In some implementations of the computer-readable storage medium, themethod may include identifying a subset of the set of candidate imagesthat are similar to the set of training images.

In some implementations of the computer-readable storage medium, themethod may include providing a graphical user interface to be displayedon a computing device. In some implementations of the computer-readablestorage medium, the graphical user interface may display a plurality ofindications corresponding to the set of candidate images. In someimplementations of the computer-readable storage medium, the method mayinclude receiving a user selection of a first indication or theplurality of indications corresponding to the first candidate image.

In some implementations of the computer-readable storage medium, themethod may include identifying a brand attribute. In someimplementations of the computer-readable storage medium, the method mayinclude selecting the first set of features to be extracted from the setof training images based at least in part on the brand attribute.

In some implementations of the computer-readable storage medium,reducing the dimensionality of each first feature tensor may includeapplying principal component analysis to each first feature tensor togenerate a first modified feature tensor for each training image.

Yet another aspect of the present disclosure relates to a systemconfigured for evaluating images. The system may include one or morehardware processors configured by machine-readable instructions. Theprocessor(s) may be configured to identify a first image. Theprocessor(s) may be configured to extract a first set of features fromthe first image to generate a first feature tensor for the first image.The processor(s) may be configured to extract a second set of featuresfrom the first image to generate a second feature tensor for the firstimage. The processor(s) may be configured to identify a second image.The processor(s) may be configured to extract a third set of featuresfrom the second image to generate a third feature tensor for the secondimage. The processor(s) may be configured to extract a fourth set offeatures from the second image to generate a fourth feature tensor forthe second image. The processor(s) may be configured to apply a firstregression analysis to determine a first geometrical distance betweenthe first feature tensor of the first image and the third feature tensorof the second image. The processor(s) may be configured to apply asecond regression analysis to determine a second geometrical distancebetween the second feature tensor of the first image and the fourthfeature tensor of the second image. The processor(s) may be configuredto determine a similarity between the first image and the second imagebased on the first geometrical distance and the second geometricaldistance.

In some implementations of the system, the processor(s) may beconfigured to calculate a similarity score representing a degree ofvisual similarity between the first image and the second image.

In some implementations of the system, the processor(s) may beconfigured to calculate a uniqueness score of the first image withrespect to the second.

In some implementations of the system, calculating the uniqueness scoreof the first image may include calculating an inverse of the similarityscore. In some implementations of the system, calculating the uniquenessscore of the first image may include identifying the inverse as theuniqueness score.

In some implementations of the system, the processor(s) may beconfigured to reduce a dimensionality of the first feature tensor priorto applying the first regression analysis to determine the firstgeometrical distance between the first feature tensor of the first imageand the third feature tensor of the second image.

In some implementations of the system, the processor(s) may beconfigured to apply a weight to the first set of features extracted fromthe first image to generate a set of weighted first features. In someimplementations of the system, the first feature tensor may be generatedbased on the set of weighted first features.

In some implementations of the system, the first set of featuresextracted from the first image may include object features. In someimplementations of the system, the processor(s) may be configured toextract the first set of features from the first image by propagatingdata corresponding to the first image through at least one networkincluding at least one of an object detection neural network, an objectclassification neural network, or an object recognition neural network.In some implementations of the system, the network may include an inputlayer, a plurality of intermediate layers, and an output layer. In someimplementations of the system, the processor(s) may be configured toextract the first set of features from the first image by extractingoutputs from at least one of the plurality of intermediate layers of thenetwork.

In some implementations of the system, extracting the first set offeatures from the first image may include extracting at least one of aset of object features, a set of scene features, a set of intensityfeatures, a set of contrast features, a set of color features, and a setof blurriness features from the first image.

Another aspect of the present disclosure relates to a method forevaluating images. The method may include identifying a first image. Themethod may include extracting a first set of features from the firstimage to generate a first feature tensor for the first image. The methodmay include extracting a second set of features from the first image togenerate a second feature tensor for the first image. The method mayinclude identifying a second image. The method may include extracting athird set of features from the second image to generate a third featuretensor for the second image. The method may include extracting a fourthset of features from the second image to generate a fourth featuretensor for the second image. The method may include applying a firstregression analysis to determine a first geometrical distance betweenthe first feature tensor of the first image and the third feature tensorof the second image. The method may include applying a second regressionanalysis to determine a second geometrical distance between the secondfeature tensor of the first image and the fourth feature tensor of thesecond image. The method may include determining a similarity betweenthe first image and the second image based on the first geometricaldistance and the second geometrical distance.

In some implementations of the method, it may include calculating asimilarity score representing a degree of visual similarity between thefirst image and the second image.

In some implementations of the method, it may include calculating auniqueness score of the first image with respect to the second.

In some implementations of the method, calculating the uniqueness scoreof the first image may include calculating an inverse of the similarityscore. In some implementations of the method, calculating the uniquenessscore of the first image may include identifying the inverse as theuniqueness score.

In some implementations of the method, it may include reducing adimensionality of the first feature tensor prior to applying the firstregression analysis to determine the first geometrical distance betweenthe first feature tensor of the first image and the third feature tensorof the second image.

In some implementations of the method, it may include applying a weightto the first set of features extracted from the first image to generatea set of weighted first features. In some implementations of the method,the first feature tensor may be generated based on the set of weightedfirst features.

In some implementations of the method, the first set of featuresextracted from the first image may include object features. In someimplementations of the method, it may include extracting the first setof features from the first image by propagating data corresponding tothe first image through at least one network including at least one ofan object detection neural network, an object classification neuralnetwork, or an object recognition neural network. In someimplementations of the method, the network may include an input layer, aplurality of intermediate layers, and an output layer. In someimplementations of the method, it may include extracting the first setof features from the first image by extracting outputs from at least oneof the plurality of intermediate layers of the network.

In some implementations of the method, extracting the first set offeatures from the first image may include extracting at least one of aset of object features, a set of scene features, a set of intensityfeatures, a set of contrast features, a set of color features, and a setof blurriness features from the first image.

Yet another aspect of the present disclosure relates to a non-transientcomputer-readable storage medium having instructions embodied thereon,the instructions being executable by one or more processors to perform amethod for evaluating images. The method may include identifying a firstimage. The method may include extracting a first set of features fromthe first image to generate a first feature tensor for the first image.The method may include extracting a second set of features from thefirst image to generate a second feature tensor for the first image. Themethod may include identifying a second image. The method may includeextracting a third set of features from the second image to generate athird feature tensor for the second image. The method may includeextracting a fourth set of features from the second image to generate afourth feature tensor for the second image. The method may includeapplying a first regression analysis to determine a first geometricaldistance between the first feature tensor of the first image and thethird feature tensor of the second image. The method may includeapplying a second regression analysis to determine a second geometricaldistance between the second feature tensor of the first image and thefourth feature tensor of the second image. The method may includedetermining a similarity between the first image and the second imagebased on the first geometrical distance and the second geometricaldistance.

In some implementations of the computer-readable storage medium, themethod may include calculating a similarity score representing a degreeof visual similarity between the first image and the second image.

In some implementations of the computer-readable storage medium, themethod may include calculating a uniqueness score of the first imagewith respect to the second.

In some implementations of the computer-readable storage medium,calculating the uniqueness score of the first image may includecalculating an inverse of the similarity score. In some implementationsof the computer-readable storage medium, calculating the uniquenessscore of the first image may include identifying the inverse as theuniqueness score.

In some implementations of the computer-readable storage medium, themethod may include reducing a dimensionality of the first feature tensorprior to applying the first regression analysis to determine the firstgeometrical distance between the first feature tensor of the first imageand the third feature tensor of the second image.

In some implementations of the computer-readable storage medium, themethod may include applying a weight to the first set of featuresextracted from the first image to generate a set of weighted firstfeatures. In some implementations of the computer-readable storagemedium, the first feature tensor may be generated based on the set ofweighted first features.

In some implementations of the computer-readable storage medium, thefirst set of features extracted from the first image may include objectfeatures. In some implementations of the computer-readable storagemedium, the method may include extracting the first set of features fromthe first image by propagating data corresponding to the first imagethrough at least one network including at least one of an objectdetection neural network, an object classification neural network, or anobject recognition neural network. In some implementations of thecomputer-readable storage medium, the network may include an inputlayer, a plurality of intermediate layers, and an output layer. In someimplementations of the computer-readable storage medium, the method mayinclude extracting the first set of features from the first image byextracting outputs from at least one of the plurality of intermediatelayers of the network.

In some implementations of the computer-readable storage medium,extracting the first set of features from the first image may includeextracting at least one of a set of object features, a set of scenefeatures, a set of intensity features, a set of contrast features, a setof color features, and a set of blurriness features from the firstimage.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of ‘a’, ‘an’,and ‘the’ include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for evaluating images, inaccordance with one or more implementations.

FIG. 2 illustrates data flow in a process for extracting features fromimages, in accordance with one or more implementations.

FIGS. 3 and 4 illustrate data flow in a process for constructing agenerative model, in accordance with one or more implementations.

FIG. 5 illustrates data flow in a process for generating a similarityscore for an image, in accordance with one or more implementations.

FIG. 6 illustrates data flow in a process for generating a similarityscore for an image, in accordance with one or more implementations.

FIG. 7 illustrates a method for evaluating images, in accordance withone or more implementations.

FIG. 8 illustrates a method for evaluating images, in accordance withone or more implementations.

DETAILED DESCRIPTION

Image-based content can be among the most important content posted byusers to web-based or online platforms, such as social media websitesand other websites. Such content can also be referred to as “creative,”and can be included as part of an advertising campaign of a business, apost from an individual that contributes to the individual's onlineimage, a graphic design composition using a software application likePhotoshop, or a photograph captured via a user's camera on a mobiledevice. Often, users (e.g., businesses or individuals) post contentitems such as images that are intended to have a common theme. Forexample, a user may post images having similar subject matter (e.g.,relating to a particular topic) or images that are intended to appeal toa group of viewers who share a particular set of demographiccharacteristics. Such a group can be referred to as a target audience orintended audience.

Selecting one or more images that are similar to a given set of imagescan be challenging. For example, a user may capture a large volume ofimages and store the images together on a computing device, such aswithin an image gallery application of a mobile phone. It can bedifficult and time consuming for the user to manually evaluate eachimage to find a group of images that share certain characteristics, suchas similarity or relevance to a common theme or subject matter.

The systems and methods described in this disclosure can implement animage evaluation mechanism which can identify a group of similar imageswithin a database, photo gallery application, or other set of images.The systems and methods of this disclosure can also be used to identifyone or more images from within a set of images that are similar to aselected candidate image. For example, various computer-implementedtechniques, including artificial intelligence and machine learningalgorithms, can be used to extract features from each digital image toevaluated. The feature extraction itself can make use of artificialintelligence techniques including object detection or recognition neuralnetworks. The extracted features can then be processed using additionalanalytical techniques to determine a degree of similarity between thefeatures of a candidate image and a set of other images or a degree ofpairwise similarity between two candidate images.

In some implementations, features can be extracted from an image via oneor more fully connected and/or convolutional layers of one or more deeplearning models. Other features can be extracted via additionalcomputational processes and from the inference results of deep learningmodels. The deep learning models can include models for objectdetection, scene detection, etc.

In some implementations, features can be represented as vectors ortensors. For example, a first set of features, such as features relatingto objects, can be extracted from an image and represented as an objectfeature tensor. A second set of features, such as features relating toscenes, can be extracted from the image and represented as a scenefeature tensor. In some implementations, any number of different featuretypes may be extracted from an image and represented as a respectivefeature tensor. This data can optionally be adjusted and thedimensionality of the resulting tensors can be reduced, for example viaprincipal component analysis (PCA). In some implementations, reducingthe dimensionality of a feature tensor can reduce the amount of data tobe processed, thereby saving time and improving computationalefficiency, while still allowing accurate similarity detection to beaccomplished.

Feature tensors for images can be used in two separate but relatedprocesses. For example, the feature tensors can be used to calculate thesimilarity of two images, which may be referred to as pairwisesimilarity. The feature tensors also can be used to calculate a degreeof similarity between one candidate image and a given set of otherimages, which may be referred to as set similarity. In the case ofpairwise similarity, the distance between the feature tensors for eachfeature or attribute can be calculated and normalized. For example, twocandidate images a and b, the distances are calculated using thefollowing formula: D₁=ABS(T1_(a)−T1_(b)), D₂=ABS(T2_(a)−T2_(b)), etc.,where D represents the scalar distance and T represents a featuretensor. The scalar values for the distance calculations can then becompared using a regression algorithm that can apply a respective weightto the different distances and combine them to produce a finalsimilarity score. For example, a similarity score may be calculatedusing the following formula: W₁D₁+W₂D₂+W₃D₃+W₄D₄, etc., where Wrepresents a respective weighting for a given feature type.

In the case of set similarity, the frequency of features in the set ofimages can be used to create a generative model of all such featurescontained in the set of images. The extracted features of a candidateimage can then be compared against these generative models to determinehow frequent the features in each tensor occur in the set of images usedto create the generative model.

In some implementations, Similarity can be used for finding othersimilar social media accounts in a database, which can be then used toderive similar images to a candidate image. In some implementations,similarity results can also be introduced into an additional machinelearning model to determine visually similar images that also appeal toa particular target audience.

In some implementations, similarity of images can be used to make visualrecommendations to users. For example, if a user sells a particularproduct through a web-based store, an image of that product can beretrieved from the user's product listing and matched for similarityagainst one or more sets of others images that may be similar, such asimages from a social media website with a theme that is relevant to theproduct. This can help to identify images similar to the user's productlisting, but which may also be more appealing to the same audience. Thena recommendation can be made to the user suggesting that the user updatethe product listing to replace the original image with a new or similarimage to improve performance.

In some implementations, the inverse of visual similarity can bereferred to as a “uniqueness” measure. For example, uniqueness can alsobe used in matching accounts, or image sets, that are uniquelyassociated with a particular brand, product, topic, event, or theme.Uniqueness is also a measure that can be calculated for a set or in apairwise manner for two or more images.

In some implementations, the systems and methods of this disclosure canprovide an image search capability of an image database, such as a stockphoto website. For example, a user may identify a copyrighted image thatthe user may not have permission to publish. The systems and methods ofthis disclosure can search an image database to find other images thatare similar to the copyright protected image, but available for publicuse, thereby providing reverse-image search functionality. Thus, byperforming similarity analysis using a set of images hosted, forexample, by a free stock photo website, the systems and methods of thisdisclosure can identify one or more visually-similar images that areroyalty-free to use. This can be highly beneficial to users searchingfor images that are free and open to use. Without the techniquesdescribed in this disclosure, identifying such images can be anextremely time consuming and computationally intensive process. In someimplementations, the systems and methods of this disclosure can alsoevaluate and rank two or more candidate images identified as similar toa give image or set of images based on their appeal to one or moreaudiences.

In some implementations, the techniques of this disclosure can implementa similarity determination process that is adjusted for weightingcertain feature types more heavily than others. For example, the systemsand methods of this disclosure can determine a degree of similaritybetween images while giving a higher weight (e.g., a greatersignificance) to object-related features in the images than to othertypes of features. In general, any combination of features can beassigned any combination of weights. For example, in someimplementations, extra weight can be given to features that may relateto similar color palettes, etc.

The subject matter described in this disclosure can be used to overcometechnical challenges relating to determining set-based or pairwisesimilarity of images. For example, it can be difficult to determine adegree of similarity of two or more digital images based on raw imagedata alone, because there may not be significant meaning in thepixel-by-pixel variation of each image that relates to how the image isperceived by a human viewer. This disclosure provides technicalsolutions to this technical challenge. For example, this disclosureprovides techniques for imitating human visual cognition by firstextracting features from images, rather than relying on the raw imagedata to determine similarity between images. As a result, the totalamount of data can be reduced relative to the raw image data, therebyallowing less computationally intensive solutions that provide a highdegree of accuracy in computing similarity between images as a humanviewer would perceive them. This can enable the model to run on computerhardware that does not require large amounts of memory.

It can also be a technical problem to identify and extract meaningfulfeatures from an image in an automated fashion. To address thistechnical challenge, in some implementations classification or detectionnetworks can be used to extract features from an image. These types ofnetworks can be used to classify an input image into one of a smallnumber of states. For example, a detection network could be used todetermine whether an image includes a dog (e.g., the model classifiesimages into two states, including one state for images that depict dogsand another state for images than do not depict dogs). Such a networkmay include nodes arranged in layers including an input layer, severalhidden layers that form a “black box,” and an output layer that providesan answer to the classification question. For many applications, theoutputs of hidden layers of a classification network may not be ofinterest because they do not answer a classification question, and sothey are often ignored. However, as described further below, the outputsof these hidden layers in a classification network that is used toprocess image data (e.g., an object detection network, a scene detectionnetwork, etc.) can provide useful information about features of an imagethat are important to human perception of the image, such as its generalsubject matter or theme. This disclosure describes techniques for usingsuch information to efficiently compare two or more images to determinea degree of similarity of the images.

FIG. 1 illustrates a system 100 configured for evaluating images, inaccordance with one or more implementations. In some implementations,system 100 may include one or more servers 105. Server(s) 105 may beconfigured to communicate with one or more client computing platforms110 according to a client/server architecture and/or otherarchitectures. Client computing platform(s) 110 may be configured tocommunicate with other client computing platforms via server(s) 105and/or according to a peer-to-peer architecture and/or otherarchitectures. Users may access system 100 via client computingplatform(s) 110.

Server(s) 105 may be configured by machine-readable instructions 115.Machine-readable instructions 115 may include one or more instructionmodules. The instruction modules may include computer program modules.The instruction modules may include one or more of a feature extractionmodule 120, an image analysis module 125, a graphical user interface(GUI) module 130, and/or other instruction modules.

Together, the feature extraction module 120, the image analysis module125, the graphical user interface (GUI) module 130, and the othercomponents of the system 100 can be configured to determine a similarityscore for a candidate image and a selected set of other images. Thus,the system 100 may first gather, collect, receive, or otherwise access aset of images against which the candidate image is to be compared. Thesystem 100 also may be configured to calculate a pairwise similarityscore representing a degree of similarity for two images. The similarityscore can represent a degree of similarity between the candidate imagethe selected set of images.

In some implementations, the image analysis module 125 may be configuredto select a set of training images. The image analysis module 125 mayalso be configured to select the set of training images based on atleast one of a common subject matter, a common author, a common origin,or a common theme. For example, the set of training images can beselected from among a set of images posted to a social media website bya particular user or author. In some implementations, the trainingimages can be selected based on an indication that the images appeal toa particular target audience or are otherwise relevant to a particulartarget audience.

Feature extraction module 120 may be configured to extract a first setof features from each training image of the set of training images togenerate a first feature tensor for each training image. The first setof features extracted from each training image may include objectfeatures. By way of non-limiting example, extracting the first set offeatures from each training image may include extracting at least one ofa set of object features, a set of scene features, a set of intensityfeatures, a set of contrast features, a set of color features, and a setof blurriness features from each training image. In someimplementations, the image analysis module 125 may be configured toselect a first set of features to be extracted from the set of trainingimages based at least in part on the brand attribute, such as a font ora color scheme of a brand logo.

The feature extraction module 120 may also be configured to extract asecond set of features from each training image to generate a secondfeature tensor for each training image. In some implementations, thefeature extraction module 120 may be configured to extract the first setof features from each training image by propagating data correspondingto each training image through at least one network including at leastone of an object detection neural network, an object classificationneural network, or an object recognition neural network. By way ofnon-limiting example, the network may include an input layer, aplurality of intermediate layers, and an output layer. In someimplementations, the feature extraction module 120 may be configured toextract the first set of features from each training image by extractingoutputs from at least one of the plurality of intermediate layers of thenetwork.

In some implementations, the image analysis module 125 may be configuredto reduce a dimensionality of each first feature tensor to generate afirst modified feature tensor for each training image. The imageanalysis module 125 may also be configured to reduce a dimensionality ofeach second feature tensor to generate a second modified feature tensorfor each training image. The image analysis module 125 may also beconfigured to reduce a dimensionality of the candidate image featuretensor to generate a modified candidate image feature tensor. Reducingthe dimensionality of each first feature tensor (or any other featuretensor) may include, for example, applying principal component analysisto each first feature tensor to generate a first modified feature tensorfor each training image. Determining whether the candidate image may besimilar to the set of training images can then include comparing themodified candidate image feature tensor with the first generative model.This can result in improved computational efficiency, relative toperforming a comparison of the unmodified (e.g., higher dimensional)candidate image feature tensor with the first generative model.

In some implementations, the image analysis module 125 may be configuredto construct a first generative model representing the first set offeatures and a second generative model representing the second set offeatures of the set of training images, based on the first modifiedfeature tensors and the second modified feature tensors of each trainingimage of the set of training images.

After the generative models have been constructed based on the featuresincluded in the training images, the image analysis module 125 may beconfigured to identify a first candidate image. For example, thecandidate image can be any image whose similarity (or dissimilarity) tothe training images is of interest. In some implementations, thecandidate image can be provided by a user. For example, the GUI module130 may be configured to provide a graphical user interface to bedisplayed on a computing device, such as the client computingplatform(s) 110. The graphical user interface may display a plurality ofindications, such as thumbnails or titles, corresponding to a set ofcandidate images. For example, the set of candidate images can be any orall of the images stored in a photo gallery application on the computingdevice. In some implementations, the GUI module 130 may be configured toreceive a user selection of a first indication or the plurality ofindications corresponding to the selected candidate image whose degreeof similarity to the training images is of interest.

In some implementations, there may be a set of candidate images, all ofwhom are to be evaluated with respect to the training set of images. Forexample, the image analysis module 125 may be configured to identify theset of candidate images including the first candidate image. The imageanalysis module 125 may be configured to determine, for each candidateimage of the set of candidate images, whether the candidate image issimilar to the set of training images based on the first candidate imageand each of the first generative model and the second generative model.The image analysis module 125 may also be configured to identify asubset of the set of candidate images that are similar to the set oftraining images. For example, the image analysis module 125 may beconfigured to apply a regression algorithm to the first candidate imageand each of the first generative model and the second generative modelto determine whether the first candidate image is similar to the set oftraining images.

Image analysis module 125 may be configured to calculate a similarityscore representing a degree of visual similarity between the firstcandidate image and the set of training images, based on the regressionalgorithm. In some implementations, the image analysis module 125 mayalso be configured to calculate a uniqueness score of the firstcandidate image with respect to the set of training images. For example,calculating the uniqueness score of the first candidate image mayinclude calculating an inverse of the similarity score. In someimplementations, the image analysis module 125 can identify the inverseas the uniqueness score.

In some implementations, the feature extraction module 120 may beconfigured to extract features from the first candidate image togenerate a candidate image feature tensor. The features may correspondto the first set of features extracted from each candidate image. Theimage analysis module 125 may also be configured to apply a weight tothe features extracted from the candidate image to generate a set ofweighted candidate image features. The candidate image feature tensormay be generated based on the set of weighted candidate image features.In some implementations, the weighting of the features can instead beapplied directly to each candidate image before the features areextracted, rather than applied to the feature tensors.

In some implementations, the image analysis module 125 may be configuredto identify respective locations of the first feature tensor and thesecond feature tensor in a feature space defined by the first set offeatures and the second set of features. The image analysis module 125may also be configured to generate a visual signature for the set oftraining images based on the respective locations of the first featuretensor and the second feature tensor. For example, a visual signaturecan include metadata relating to the content of a visual fingerprint. Avisual fingerprint can be a representation of a single image derivedfrom the feature extraction processes described above. The visualfingerprint can indicate the important attributes of a single image andcan help to distinguish and compare individual images against oneanother. The visual fingerprint of individual images collectively canenable a similarity classifier, which may be a similarity score.

The visual signature can be derived from all the visual fingerprints ofan image collection. For example, the visual signature can be or canrepresent an overall distribution of visual fingerprints for acollection of images. For example, this can be derived from a user'simage library, where the visual fingerprints of the images in the user'slibrary are used to generate an overall signature for that user'slibrary. That signature can in turn be used as a metric to compare oneuser's image library against another user's image library to determinehow similar those image libraries are. Thus, analysis can be conductedupon visual fingerprints in a visual signature (e.g. similarity scoresfor similarity classifiers, clustering of feature tensor locations inmultidimensional space, performance scores, labels, etc.) This canprovide a means of generating a surface match for any query of a visualsignature. In some implementations, additional analysis can also beconducted using the original fingerprints from which the visualsignature was derived.

In some implementations, the system 100 may be configured to calculate apairwise similarity score representing a degree of similarity for twoimages, rather than a set-based similarity for a candidate image asdescribed above. For example, the image analysis module 125 may beconfigured to identify a first image and a second image whose degreesimilarity to one another is of interest. In some implementations, thefeature extraction module 120 may be configured to extract a first setof features from the first image to generate a first feature tensor forthe first image. The first set of features extracted from the firstimage may include object features. By way of non-limiting example,extracting the first set of features from the first image may includeextracting at least one of a set of object features, a set of scenefeatures, a set of intensity features, a set of contrast features, a setof color features, and a set of blurriness features from the firstimage. The feature extraction module 120 may also be configured toextract a second set of features from the first image to generate asecond feature tensor for the first image.

In some implementations, the feature extraction module 120 may beconfigured to extract a third set of features from the second image togenerate a third feature tensor for the second image. The featureextraction module 120 may also be configured to extract a fourth set offeatures from the second image to generate a fourth feature tensor forthe second image.

In some implementations, the feature extraction module 120 may beconfigured to extract the first set of features from the first image bypropagating data corresponding to the first image through at least onenetwork including at least one of an object detection neural network, anobject classification neural network, or an object recognition neuralnetwork. By way of non-limiting example, the network may include aninput layer, a plurality of intermediate layers, and an output layer.The feature extraction module 120 may be configured to extract the firstset of features from the first image by extracting outputs from at leastone of the plurality of intermediate layers of the network.

In some implementations, the image analysis module 125 may be configuredto apply a first regression analysis to determine a first geometricaldistance between the first feature tensor of the first image and thethird feature tensor of the second image. The image analysis module 125may also be configured to apply a second regression analysis todetermine a second geometrical distance between the second featuretensor of the first image and the fourth feature tensor of the secondimage.

In some implementations, the image analysis module 125 may be configuredto determine a similarity between the first image and the second imagebased on the first geometrical distance and the second geometricaldistance. Image analysis module 125 may be configured to calculate asimilarity score representing a degree of visual similarity between thefirst image and the second image. In some implementations, the imageanalysis module 125 may be configured to calculate a uniqueness score ofthe first image with respect to the second. Calculating the uniquenessscore of the first image may include calculating an inverse of thesimilarity score. For example, the image analysis module 125 mayidentify the inverse as the uniqueness score.

In some implementations, the image analysis module 125 may be configuredto reduce a dimensionality of the first feature tensor prior to applyingthe first regression analysis to determine the first geometricaldistance between the first feature tensor of the first image and thethird feature tensor of the second image. In some implementations, theimage analysis module 125 may be configured to apply a weight to thefirst set of features extracted from the first image to generate a setof weighted first features. The first feature tensor may be generatedbased on the set of weighted first features.

FIGS. 2-6 depict processes for extracting features from images,constructing a generative model, and using the generative model todetermine a similarity score for a candidate image with respect to a setof training images or with respect to a single second image. Theprocesses depicted in FIGS. 2-6 can be implemented, for example, by theserver 102 of FIG. 1 . Thus, FIGS. 2-6 are described below withreference also to FIG. 1 . Referring now to FIG. 2 , data flow in aprocess for extracting features from images is illustrated, inaccordance with one or more implementations. The process 200 can beperformed, for example, by the feature extraction module 120 of FIG. 1 .It should be understood that, while FIG. 2 shows feature extraction fora single image 202, the process 200 can be repeated for any number ofimages included in a set of images, such as images in a database orphoto gallery application. The process 200 can include using one or moreartificial intelligence models 204, one or more computer vision services206, and other data analysis techniques 208 to extract features from theimage 202.

In some implementations, the feature extraction module 120 can implementthe one or more artificial intelligence models 204, the one or morecomputer vision services 206, and the other data analysis techniques208. For example, the one or more artificial intelligence models 204,the one or more computer vision services 206, and the other dataanalysis techniques 208 can each include an artificial neural networkthat includes nodes arranged in a plurality of layers. Each node can bea computational unit, which may also be referred to as an artificialneuron. The layers can be arranged sequentially such that a nodereceives an input signal from one or more of the nodes in the previouslayer, processes the input according to a function to produce an output,and transmits the output to one or more nodes of the next layer. Thefirst layer of such a network can be referred to as an input layer, andcan receive the raw image data (e.g., data corresponding to eachindividual pixel of the image 202). The final layer can be referred toas an output layer. Thus, the image data for the image 202 can bepropagated through the layers of an artificial neural network to causethe artificial neural network to produce one or more outputs at eachlayer of the artificial network, including the final or output layer.

In some implementations, any of the one or more artificial intelligencemodels 204, the one or more computer vision services 206, and the otherdata analysis techniques 208 can be a detection network. For example, adetection network can be configured to determine a presence or absenceof one or more predetermined characteristics of the image 202, such asthe features of a scene depicted in the image 202, the features ofobjects depicted in the image 202, a color or colors most prevalent inthe image 202, etc. Each such network can be used to extract arespective set of image features 210 from the image 202. Thus, a scenedetection network can be used to extract a set of scene features fromthe image 202, an object detection network can be used to extract a setof object features from the image 202, etc.

In some implementations, the feature extraction module 120 can use theoutputs of an intermediate layer of an artificial neural networkcorresponding to any of the one or more artificial intelligence models204, the one or more computer vision services 206, and the other dataanalysis techniques 208. An intermediate layer can be any layer betweenthe input layer and the output layer. Thus, while a detection networkmay have an output layer that outputs a binary signal (e.g., indicatingpresence or absence of a particular trait in the image 202), the outputsof intermediate layers also can be relevant to image features 210 in theimage 202. In some implementations, these intermediate outputs can bemathematically descriptive of the image 202 itself. In someimplementations, the feature extraction module 120 can extract the imagefeatures 210 based on the outputs of an intermediate layer of anartificial neural network (e.g., any of the one or more artificialintelligence models 204, the one or more computer vision services 206,and the other data analysis techniques 208), which may be represented asa vector, a tensor, or any other form of information.

The image features 210 that can be extracted from the image 202 by thefeature extraction module 120 are not limited to object, scene, or colorfeatures. For example, the features extracted from the image 202 can beor can include any stylistic features that may relate to any visualcharacteristic of an image, such as layout, position, symmetry, balance,arrangement, composition, pixel intensity, contrast, blurriness, objectlocation, depth of field, angle of view, focal point, view point,vantage point, foreground/background content, white space/negativespace, cropping, framing, color scheme, hue, tint, temperature, tone,saturation, brightness, shade, mood, line, angles, noise, contours,gradients, texture, repetition, patterns, blowout, blooming,concentricity, cubic attributes, geometric attributes, shadow, blockedshadow, vignetting, scale, number of objects, position of objects,spatial context, proportion, shapes, shape of objects, number of shapes,attributes of objects, form, perspective, representation, path, scenery,time of day, exposure, time lapse, typography, position of headline,size of headline, length of text, location of call-to-action, typeface,font, location of faces, posture/pose of people, location of figures,gestures, action/activities of people, number of people, hair color ofpeople, ethnicity of people, gender of people, age of people,expressions and emotions of people, facial attributes, clothing andappearance, accessories, resolution, orientation, icons, emojis, logos,watermarks, etc. It should be understood that this list of attributes isexemplary only, and should be not read as limiting the scope of thisdisclosure.

Other types of features of the images in the training dataset also canbe extracted from the image 202. It should be understood that while theimage features 210 are depicted as a single entity in FIG. 2 forillustrative purposes, in some implementations separate sets of imagefeatures 210 may be extracted by each of the one or more artificialintelligence models 204, the one or more computer vision services 206,and the other data analysis techniques 208. The image feature manager110 can process these separate sets of features, for example by alteringa format of the feature sets or combining the feature sets, to producethe image features 210. In some implementations, the image features 210can be represented mathematically as one or more feature tensors. Forexample, a respective feature tensor can be generated for each of one ormore feature types for the image 202. In some implementations, theprocess 200 can include reducing a dimensionality of one or more featuretensors or other data structures used to implement the image features210. For example, dimensionality can be reduced by applying ananalytical technique such as principal component analysis to one or moreof the tensors or other data structures used to represent the imagefeatures 210. In some implementations, reducing the dimensionality canhelp to reduce the overall size of the image features 210. The featureextraction module 120 can store the image features 210 in a data store212. In some implementations, the data store 212 can correspond toelectronic storage 165 of FIG. 2 .

FIGS. 3 and 4 illustrate data flow in a process for constructing agenerative model, in accordance with one or more implementations.Additional, fewer, or different operations may be performed in process300 and process 400. The process 300 of FIG. 3 and the process 400 ofFIG. 4 can make use of the image features 210 extracted in the process200 of FIG. 2 . For example, as shown in FIG. 3 , the image features 210can be retrieved from the data store 212 and processed, manipulated, orotherwise used to construct a generative model 304. In someimplementations, the generative model can be a mathematical model (e.g.,data stored in one or more data structures) that represents thedistribution of image features 210 within the image 202. For example, insome implementations, the generative model can be a mathematical modelthat represents the joint probability distribution of image features 210within the image 202.

In some implementations, more than one generative model may be produced.For example, as shown in the process 400 of FIG. 4 , multiple generativemodels 304 a-304 c can be produced based on the set of image features210. As shown, the image features 210 can be divided into subsets ofimage features 210 a, 210 b, and 210 c. Generally, the subsets of imagefeatures 210 a, 210 b, and 210 c can be non-overlapping with oneanother. In some implementations, the subsets of image features 210 a,210 b, and 210 c can be grouped according to categories or types offeatures, such as object features, scene features, color features, etc.Thus, each subset 210 a, 210 b, and 210 c of the image features 210 canbe represented as a respective tensor that contains information aboutone type or category of features included in the image features 210. Insome implementations, a respective generative model 304 a, 304 b, and304 c can be constructed, based on each of the subsets 210 a, 210 b, and210 c of the image features 210. Thus, the generative model 304 a canrepresent the distribution of the subset of image features 210 a, thegenerative model 304 b can represent the distribution of the subset ofimage features 210 b, and the generative model 304 c can represent thedistribution of the subset of image features 210 c. In someimplementations, the generative models 304 a-304 c can be combined intoa single generative model 304.

FIGS. 3 and 4 show the general approach for propagating image features210 for a single image 202 through the layers of the machine learningmodel 304 in order to train the model. It should be appreciated thatthese processes may be repeated with image features 210 from the otherimages 202 in the set of training images, to produce one or moregenerative models 304 for each image in the set of training images.

FIG. 5 illustrates data flow in a process 500 for generating asimilarity score for a candidate image 504, in accordance with one ormore implementations. Additional, fewer, or different operations may beperformed in process 500. In some implementations, the process 500 canbe performed by the image analysis module 125 of FIG. 1 . The process500 can make use of the one or more generative models 304 that has beenconstructed for each image in the set of training images according tothe processes 300 and 400 shown in FIGS. 3 and 4 , respectively. Thecandidate image 504 can be any image whose similarity to the set oftraining images is of interest. For example, a user of one of the clientcomputing devices 104 may submit the candidate image 504 for asimilarity determination. In some implementations, a user may submitmore than one candidate image 504 and each candidate image 504 can bescored separately to determine its similarity with respect to the set oftraining images, using the process 500.

In some implementations, data flow for scoring the candidate image 504can be similar to data flow for constructing the generative model 304with each training image. For example, a set of image features 510 canbe extracted from the candidate image 504. In some implementations, theimage features 510 can be extracted from the candidate image 504 usingthe same or similar techniques described above for extracting the imagefeatures 210 from an image 202. For example, as show in FIG. 2 , one ormore AI models 204, one or more computer vision services 206, and otherdata analysis techniques 208 can be used to extract features from thecandidate image 504. In some implementations, the one or more AI models204, the one or more computer vision services 206, and the other dataanalysis techniques 208 may be or may include artificial neural networkshaving layered structures, and features may be extracted fromintermediate layers of these artificial neural networks. In someimplementations, dimensionality of one or more feature tensors includedin the image features 510 can be reduced using techniques similar tothose described above.

The problem of calculating a degree of similarity between the candidateimage 504 and the set of training images can be a regression problem.For example, a regression algorithm 520 can be used to compare the imagefeatures 510 of the candidate image with each of the generative models304 a, 304 b, and 304 c for the images included in the training set.Thus, the image analysis module 125 can be configured to apply theregression algorithm 520 to compare feature tensors included in theimage features 510 of the candidate image with the correspondinggenerative models 304 a representing the training images. In someimplementations, the regression algorithm 520 can be used to compute adistance (e.g., a geometric distance in a space defined by the imagefeatures 510) between the image features 510 of the candidate image andthe image features 210 of each training image (as represented by thegenerative models 304 a, 304 b, and 304 c). The image analysis module125 can calculate a similarity score 522 based on the results of theregression algorithm 520. For example, if the regression algorithm 520indicates that the image features 510 are relatively close to thegenerative models 304 a, 304 b, and 304 c, the image analysis module 125can calculate a similarity score 522 indicating that the candidate image504 is relatively similar to the set of training images. If theregression algorithm 520 indicates that the image features 510 arerelatively far from the generative models 304 a, 304 b, and 304 c, theimage analysis module 125 can calculate a similarity score 522indicating that the candidate image 504 is relatively dissimilar to theset of training images. In some implementations, the similarity scorecan be a numerical value. For example, the similarity score may be aninteger between zero and 100, or a decimal value between 0 and 1.

FIG. 6 illustrates data flow in a process 600 for generating asimilarity score for an image, in accordance with one or moreimplementations. Additional, fewer, or different operations may beperformed in process 600. In some implementations, the process 600 canbe performed by the image analysis module 125 of FIG. 1 . The process600 can be used to compute pairwise similarity between a first image 602a and a second image 602 b. This differs from the process 500 in thatthe process 500 is used to compute set similarity (i.e., similarity of acandidate image with respect to a set of training images) rather thanpairwise similarity (i.e., a degree of similarity between two candidateimages). The first image 602 a and the second image 602 b can be anyimages whose similarity to one another is of interest. For example, auser of one of the client computing devices 104 may submit the firstimage 602 a and the second image 602 b a similarity determination.

In some implementations, data flow for determining similarity betweenthe first image 602 a and the second image 602 b can be similar to dataflow for constructing the generative model 304 with each training imageas described above in FIGS. 2-4 . For example, a set of image featurescan be extracted from each of the first image 602 a and the second image602 b. In some implementations, multiple sets of images can be extractedfrom each of the first image 602 a and the second image 602 b. Forexample, as shown in FIG. 6 , three sets of image features (labeled 610a-610 c) can be extracted from the first image 602 a, and three sets ofimage features (labeled 610 d-610 f) can be extracted from the secondimage 602 b. In some implementations, the image features 510 can beextracted from the candidate image 504 using the same or similartechniques described above for extracting the image features 210 from animage 202. For example, as show in FIG. 2 , one or more AI models 204,one or more computer vision services 206, and other data analysistechniques 208 can be used to extract features from the candidate image504. In some implementations, the one or more AI models 204, the one ormore computer vision services 206, and the other data analysistechniques 208 may be or may include artificial neural networks havinglayered structures, and features may be extracted from intermediatelayers of these artificial neural networks.

In some implementations, the image features 610 a-610 c can berepresented as tensors. The dimensionality of one or more of thesefeature tensors can be reduced using techniques similar to thosedescribed above. In some implementations, the image features 610 a-610 ccan be feature types or categories that correspond to the types ofcategories of image features 610 d-610 f, respectively. This canfacilitate comparison of pairs of the image features 610 a-610 d via theregression algorithm 620. For example, the image features 610 a of thefirst image 602 a can be compared to the image features 610 d of thesecond image 602 b, the image features 610 b of the first image 602 acan be compared to the image features 610 e of the second image 602 b,and the image features 610 c of the first image 602 a can be compared tothe image features 610 f of the second image 602 b.

Similar to the calculation of the similarity score 522 as shown in FIG.5 , the calculation of the pairwise similarity score 622 can be aregression problem. For example, the regression algorithm 620 can beused to compare the image features 610 a-610 b of the first image 602 awith the image features 610 c-610 f, respectively, of the second image602 b. Thus, the image analysis module 125 can be configured to applythe regression algorithm 620 to compare feature tensors included in theimage features 610 of the first image 602 a with the corresponding imagefeatures 610 of the second image 602 b. In some implementations, theregression algorithm 620 can be used to compute a distance (e.g., ageometric distance in a space defined by the image features 610) betweenthe feature tensors included in the image features 610 of the firstimage 602 a with the corresponding image features 610 of the secondimage 602 b. The image analysis module 125 can calculate a pairwisesimilarity score 622 based on the results of the regression algorithm620. For example, if the regression algorithm 620 indicates that theimage features 610 a-610 c are relatively close to the image features610 d-610 f, the image analysis module 125 can calculate a pairwisesimilarity score 622 indicating that the first image 602 a is relativelysimilar to the second image 602 b. If the regression algorithm 620indicates that the image features 610 a-610 c are relatively far fromthe image features 610 d-610 f, the image analysis module 125 cancalculate a pairwise similarity score 622 indicating that the firstimage 602 a is relatively dissimilar from the second image 602 b.

In some implementations, the similarity score can be a numerical value.For example, the pairwise similarity score 622 may be an integer betweenzero and 100, or a decimal value between 0 and 1. In someimplementations, the image analysis module 125 can apply a normalizationtechnique when calculating the pairwise similarity score 622. Forexample, the normalization technique can make use of a set of parametersthat may be selected based on the feature types of the features 610a-610 f In some implementations, a pairwise similarity score 622 nearzero may indicate a relatively high degree of similarity between thefirst image 602 a and the second image 602 b, while a pairwisesimilarity score 622 near 1 may indicate a relatively low degree ofsimilarity between the first image 602 a and the second image 602 b.

In some implementations, the normalization technique for pairwisesimilarity can rely on two parameters, including an upper bound and alower bound for the distance between the same feature tensor for thefirst image 602 a and the second image 602 b images. The value of thedistance can then be normalized using the following equation: (distancebetween the two tensors−lower bound)/(upper bound−lower bound). In someimplementations, the results can be clipped at 0 and 1, so that anydistance values lower than the lower bound are set to 0 and distancehigher than the upper bound are set to 1. In this example, a lower scorecan indicate that the first image 602 a and the second image 602 b aremore similar for a given feature tensor, and a higher score can indicateless similarity for that feature. The upper and lower bounds can bedetermined, for example, by evaluating feature tensors for a largenumber of images for each feature.

In some implementations, server(s) 105, client computing platform(s)110, and/or external resources 160 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 105, clientcomputing platform(s) 110, and/or external resources 160 may beoperatively linked via some other communication media.

A given client computing platform 110 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given client computing platform 110 to interface with system 100and/or external resources 160, and/or provide other functionalityattributed herein to client computing platform(s) 110. By way ofnon-limiting example, the given client computing platform 110 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

External resources 160 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 160 may beprovided by resources included in system 100.

Server(s) 105 may include electronic storage 165, one or more processors170, and/or other components. Server(s) 105 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 105 in FIG.1 is not intended to be limiting. Server(s) 105 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 105. Forexample, server(s) 105 may be implemented by a cloud of computingplatforms operating together as server(s) 105.

Electronic storage 165 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 165 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)105 and/or removable storage that is removably connectable to server(s)105 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 165 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 165 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 165 may store softwarealgorithms, information determined by processor(s) 170, informationreceived from server(s) 105, information received from client computingplatform(s) 110, and/or other information that enables server(s) 105 tofunction as described herein.

Processor(s) 170 may be configured to provide information processingcapabilities in server(s) 105. As such, processor(s) 170 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 170 is shown in FIG. 1 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 170 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 170 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 170 may beconfigured to execute modules 120, 125, and 130, and/or other modules.Processor(s) 170 may be configured to execute modules 120, 125, and 130,and/or 205, and/or other modules by software; hardware; firmware; somecombination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor(s) 170.As used herein, the term “module” may refer to any component or set ofcomponents that perform the functionality attributed to the module. Thismay include one or more physical processors during execution ofprocessor readable instructions, the processor readable instructions,circuitry, hardware, storage media, or any other components.

It should be appreciated that although modules 120, 125, and 130 areillustrated in FIG. 1 as being implemented within a single processingunit, in implementations in which processor(s) 170 includes multipleprocessing units, one or more of modules 120, 125, and 130 may beimplemented remotely from the other modules. The description of thefunctionality provided by the different modules 120, 125, and 130described below is for illustrative purposes, and is not intended to belimiting, as any of modules 120, 125, and 130 may provide more or lessfunctionality than is described. For example, one or more of modules120, 125, and 130 may be eliminated, and some or all of itsfunctionality may be provided by other ones of modules 120, 125, and130. As another example, processor(s) 170 may be configured to executeone or more additional modules that may perform some or all of thefunctionality attributed below to one of modules 120, 125, and 130.

FIG. 7 illustrates a method 700 for evaluating images, in accordancewith one or more implementations. The operations of method 700 presentedbelow are intended to be illustrative. In some implementations, method700 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 700 areillustrated in FIG. 7 and described below is not intended to belimiting.

In some implementations, method 700 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 700 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 700.

In some implementations, the method 700 can be used to determine adegree of similarity between a candidate image and a set of trainingimages. An operation 705 may include selecting a set of training images.Operation 705 may be performed by one or more hardware processorsconfigured by machine-readable instructions including themachine-readable instructions 115 and/or any of the modules implementedby the machine-readable instructions 115, in accordance with one or moreimplementations.

An operation 710 may include extracting a first set of features fromeach training image of the set of training images to generate a firstfeature tensor for each training image. Operation 710 may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar tofeature extraction module 120, in accordance with one or moreimplementations.

An operation 715 may include extracting a second set of features fromeach training image to generate a second feature tensor for eachtraining image. Operation 715 may be performed by one or more hardwareprocessors configured by machine-readable instructions including amodule that is the same as or similar to feature extraction module 120,in accordance with one or more implementations.

In some implementations, the method 700 may optionally include reducinga dimensionality of each first feature tensor to generate a firstmodified feature tensor for each training image. For example,dimensionality reduction of each first feature tensor may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to imageanalysis module 125, in accordance with one or more implementations.

In some implementations, the method 700 may include reducing adimensionality of each second feature tensor to generate a secondmodified feature tensor for each training image. For example,dimensionality reduction of each second feature tensor may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to imageanalysis module 125, in accordance with one or more implementations.

An operation 720 may include constructing a first generative modelrepresenting the first set of features and a second generative modelrepresenting the second set of features of the set of training images,based on the first modified feature tensors and the second modifiedfeature tensors of each training image of the set of training images.Operation 720 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to image analysis module 125, in accordance withone or more implementations.

An operation 725 may include identifying a first candidate image.Operation 725 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to image analysis module 125, in accordance withone or more implementations.

An operation 730 may include applying a regression algorithm to thefirst candidate image and each of the first generative model and thesecond generative model to determine whether the first candidate imageis similar to the set of training images. Operation 730 may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to imageanalysis module 125, in accordance with one or more implementations.

FIG. 8 illustrates a method 800 for evaluating images, in accordancewith one or more implementations. The operations of method 800 presentedbelow are intended to be illustrative. In some implementations, method800 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 800 areillustrated in FIG. 8 and described below is not intended to belimiting.

In some implementations, method 800 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 800 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 800.

In some implementations, the method 800 can be used to determine adegree of similarity between a first image and a second image. Anoperation 805 may include identifying a first image. Operation 805 maybe performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to image identifying module 120, in accordance with one or moreimplementations.

An operation 810 may include extracting a first set of features from thefirst image to generate a first feature tensor for the first image.Operation 810 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to feature extraction module 120, in accordancewith one or more implementations.

An operation 815 may include extracting a second set of features fromthe first image to generate a second feature tensor for the first image.Operation 815 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to feature extraction module 120, in accordancewith one or more implementations.

An operation 820 may include identifying a second image. Operation 820may be performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to image identifying module 120, in accordance with one or moreimplementations.

An operation 825 may include extracting a third set of features from thesecond image to generate a third feature tensor for the second image.Operation 825 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to feature extraction module 120, in accordancewith one or more implementations.

An operation 830 may include extracting a fourth set of features fromthe second image to generate a fourth feature tensor for the secondimage. Operation 830 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to feature extraction module 120, in accordancewith one or more implementations.

An operation 835 may include applying a first regression analysis todetermine a first geometrical distance between the first feature tensorof the first image and the third feature tensor of the second image.Operation 835 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to regression analysis applying module 130, inaccordance with one or more implementations.

An operation 840 may include applying a second regression analysis todetermine a second geometrical distance between the second featuretensor of the first image and the fourth feature tensor of the secondimage. Operation 840 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to regression analysis applying module 130, inaccordance with one or more implementations.

An operation 845 may include determining a similarity between the firstimage and the second image based on the first geometrical distance andthe second geometrical distance. Operation 845 may be performed by oneor more hardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to similaritydetermination module 135, in accordance with one or moreimplementations.

The technology described in this disclosure for determining set-based orpairwise similarity of digital images can be implemented for a varietyof purposes. For example, in an image search application, similarityscores based on visual content items can be used to find similar contentitems automatically to provide users with more relevant results. Theapproach described herein, which examines features of a variety ofdigital images, can allow such searches to be performed withoutrequiring any other labels or referencing other metadata of the images.Thus, unlabeled digital images can be searched efficiently.

In a related example, image similarity can be leveraged to power newcontent recommendations that can be delivered to an end user. Forexample, on a web-based storefront product listing page, a user may wishto find a better lifestyle image to publish to make the user's productsappear more visually engaging. The technology described in thisdisclosure can take this input image and look for the nearest matchesacross one or more data stores. For example, the recommended imagerycould have a provenance on a stock photo site. Alternatively, arecommended image could be in a folder stored on the user's computer(e.g., in a photo gallery application executed by the computer), or itcould be in a cloud-based digital asset management system or othercontent library, social media account, etc.

In some implementations, the technology described herein can allow avisual signature to be generated for a set of one or more images. Visualsimilarity provides a means to generate a visual signature for a userbased upon the images in the user's photo gallery, or in a givencollection of images associated with the user. For example, by selectinga set of images associated with a user and determining a visualsignature of the selected set, the visual signature can also beassociated with the user. Similarly, the visual signature can beassociated with a website or other web-based property, an author, asocial media account, etc., simply by selecting the set of images fromthe web-based property, the author, the social media account anddetermining a visual signature based on the selected set of images. Thevisual signature can be a unique identifier of the user and can enablecomparison of one user against another user to find a visual match(e.g., visual signatures that are similar to one another, or near to oneanother geometrically in a space defined by features that were used togenerate the visual signatures). This is advantageous because iteliminates any need to review any demographic or personal identifiableinformation about the users to create these such a pairing. It enables anew user graph (e.g., a social network) that can be powered by thevisual connectedness between users and groups of users.

In a similar example, the similarity technology can be used as apreprocessing step before computing an identity calculation, which canbe based on the visual signature of images captured by, stored by, ownedby, or otherwise associated with a user. That is, the technologydescribed herein can be used as a content classifier to find all imagesrelating to a common theme, such as travel-related photos in the user'sphoto gallery application.

The visual similarity technology described herein can also be applied inthe context of determining whether a given image is a “fit” with auser's chosen or preferred identity, “brand look,” or other creativerequirements of a user or brand. For example, a brand may have a logothat includes a distinctive green color as part of its brand identity.In their marketing and advertising promotions, this color can thereforebe emphasized to build familiarity among potential customers andreinforce associations with the brand with end consumers. In anillustrative example, the technology described herein can be used toevaluate the visual features of a representative sample of imagery forthe brand, and can compare new candidate images to this sample set basedon their visual similarity to the brand attributes. In that way, imagesthat may not have the same green color (or a certain prominence of greencolor, etc.) may be identified as visually dissimilar and thereforeclassified as more likely to be off-brand. For example, “off-brand”images may be inappropriate for use due to potential confusion caused byinconsistent themes in messaging.

The visual similarity technology described herein can also be anextremely efficient means to compile training sets for a particularimage-based classification problem. For example, if a user wishes tobuild a classifier on “apple orchards,” the user can submit one or moreimages of an apple orchard to the systems and methods of this disclosurecan analyze the constituent visual features of the apple orchard seedimage(s) and use these features as a basis for automatically identifyingvisually-similar images. This can be used to identify a set of visuallysimilar images that can be used to train a new machine learning model,for example, without requiring the traditional heavily-manual humancuration process.

In a related example, the technology described herein can be madeavailable as a self-service option to users. With this technology, usersadvantageously do not need to have any deep learning or data scienceexperience to build a dataset. For example, the system can do this workfor them—they need only define the parameters on which they would like acustom content classifier to be generated. In some implementations, auser can access a system providing this functionality, such as theserver 105, via a network connection to the user's computing device,such as the client computing platform 110 as shown in FIG. 1 .

The technology described in this disclosure can also be utilized togenerate predicted business outcomes of visual material. For example,consider a social media post where the return on investment (ROI) oftaking a new photo is measured by a number of “likes” earned by thepost. Given a candidate image, the technology described herein can beused to identify the most visually similar contents that have beenpreviously posted, with respect to the candidate image. Each of thesehistorical contents can be associated with a number of Likes. The visualsimilarity technology described herein can be used to evaluate thefeatures of the candidate image with these other images (eitherpublished by the user, or e.g. published by other users, such as acompetitor) and then based upon a visual similarity match to thecontents that have been tagged with performance information, thetechnology described herein can be used to impute a predicted range ofLikes for the candidate image. This is useful because it provides userswith a predictive return on investment of their photo selections priorto putting them into market. This same approach can be leveraged withother forms of visual media such as advertisements, coupons, packagingdesigns, displays, website images, etc.

In a related example, users can select one or more images from acollection (e.g. on a mobile phone photo gallery application) tocustomize the content they wish to study. These selections can be usedto inform subsequent contents that are shown to or flagged for the user.Advantageously, a user's selections can also form the basis for smartalbums that automatically populate themselves with new contents. Forexample, if a user selects five images containing beer-related imagesthe system can use these images as the reference set for identifying newimages that “visually fit” with the beer-related contents. In that way,the next time a user takes a photo that relates to beer, the photo canbe automatically tagged and classified into the album withvisually-similar material. These albums can also be generatedautomatically on-device based on visual similarity without the user'scustomization if desired.

In another related example, the task of retrieving visually-similarcontent items can be performed by the end user. For example, if a userselects an image, clicks in image, or otherwise ‘chooses’ one or moreimages displayed in a photo gallery application, the technologydescribed herein can be used to retrieve or “summon” other images thatare visually similar to the user's selection. For example, the retrievedimages can be based on the similarity scores with respect to the imagesselected by the user. Such a feature can be advantageous to quickly findthe images that a user is are looking for in a disorganized space, suchas photo gallery application. Visual similarity therefore enables a newsuite of content management and curation features that improve userexperience and enable the discoverability of new and relevant content.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

What is claimed is:
 1. A system comprising: one or more hardwareprocessors having machine-readable instructions to: select a set oftraining images; extract a first set of features from each trainingimage of the set of training images to generate a first feature tensorfor each training image; extract a second set of features from eachtraining image to generate a second feature tensor for each trainingimage; construct a first generative model representing the first set offeatures and a second generative model representing the second set offeatures of the set of training images, based on the first featuretensors and the second feature tensors of each training image of the setof training images; identify a first candidate image; and apply aregression algorithm to the first candidate image and each of the firstgenerative model and the second generative model to determine whetherthe first candidate image is similar to the set of training images. 2.The system of claim 1, wherein the one or more hardware processorsfurther include machine-readable instructions to: calculate a similarityscore representing a degree of visual similarity between the firstcandidate image and the set of training images, based on the regressionalgorithm.
 3. The system of claim 2, wherein the one or more hardwareprocessors further include machine-readable instructions to: calculate auniqueness score of the first candidate image with respect to the set oftraining images.
 4. The system of claim 3, wherein calculating theuniqueness score of the first candidate image comprises: calculating aninverse of the similarity score; and identifying the inverse as theuniqueness score.
 5. The system of claim 1, wherein the one or morehardware processors further include machine-readable instructions to:extract features from the first candidate image to generate a candidateimage feature tensor, the features corresponding to the first set offeatures extracted from each candidate image, wherein determiningwhether the candidate image is similar to the set of training imagescomprises comparing the candidate image feature tensor with the firstgenerative model.
 6. The system of claim 5, wherein the one or morehardware processors further include machine-readable instructions to:apply a weight to the features extracted from the candidate image togenerate a set of weighted candidate image features, wherein thecandidate image feature tensor is generated based on the set of weightedcandidate image features.
 7. The system of claim 1, wherein: the firstset of features extracted from each training image comprises objectfeatures; and wherein the one or more hardware processors are furtherconfigured by machine-readable instructions to extract the first set offeatures from each training image by: propagating data corresponding toeach training image through at least one neural network including atleast one of an object detection neural network, an objectclassification neural network, or an object recognition neural network,wherein the neural network comprises an input layer, a plurality ofintermediate layers, and an output layer; and extracting outputs from atleast one of the plurality of intermediate layers of the network.
 8. Thesystem of claim 1, wherein extracting the first set of features fromeach training image comprises extracting at least one of a set of objectfeatures, a set of scene features, a set of intensity features, a set ofcontrast features, a set of color features, and a set of blurrinessfeatures from each training image.
 9. The system of claim 1, wherein theone or more hardware processors further include machine-readableinstructions to: identify respective locations of the first featuretensor and the second feature tensor in a feature space defined by thefirst set of features and the second set of features; generate a visualsignature for the set of training images based on the respectivelocations of the first feature tensor and the second feature tensor. 10.The system of claim 9, wherein the one or more hardware processorsfurther include machine-readable instructions to select the set oftraining images based on at least one of a common author, a commonorigin, or a common theme.
 11. The system of claim 1, wherein the one ormore hardware processors further include machine-readable instructionsto: identify a set of candidate images including the first candidateimage; determine, for each candidate image of the set of candidateimages, whether the candidate image is similar to the set of trainingimages based on the first candidate image and each of the firstgenerative model and the second generative model; and identify a subsetof the set of candidate images that are similar to the set of trainingimages.
 12. The system of claim 1, wherein the one or more hardwareprocessors further include machine-readable instructions to: provide agraphical user interface to be displayed on a computing device, thegraphical user interface displaying a plurality of indicationscorresponding to the set of candidate images; and receive a userselection of a first indication or the plurality of indicationscorresponding to the first candidate image.
 13. The system of claim 1,wherein the one or more hardware processors further includemachine-readable instructions to: identify a brand attribute; and selectthe first set of features to be extracted from the set of trainingimages based at least in part on the brand attribute.
 14. A methodcomprising: selecting a set of training images; extracting a first setof features from each training image of the set of training images togenerate a first feature tensor for each training image; extracting asecond set of features from each training image to generate a secondfeature tensor for each training image; constructing a first generativemodel representing the first set of features and a second generativemodel representing the second set of features of the set of trainingimages, based on the first feature tensors and the second featuretensors of each training image of the set of training images;identifying a first candidate image; and applying a regression algorithmto the first candidate image and each of the first generative model andthe second generative model to determine whether the first candidateimage is similar to the set of training images.
 15. The method of claim14, further comprising: calculating a similarity score representing adegree of visual similarity between the first candidate image and theset of training images, based on the regression algorithm.
 16. Themethod of claim 15, further comprising: calculating a uniqueness scoreof the first candidate image with respect to the set of training images.17. The method of claim 16, wherein calculating the uniqueness score ofthe first candidate image comprises: calculating an inverse of thesimilarity score; and identifying the inverse as the uniqueness score.18. The method of claim 14, further comprising: extracting features fromthe first candidate image to generate a candidate image feature tensor,the features corresponding to the first set of features extracted fromeach candidate image, wherein determining whether the candidate image issimilar to the set of training images comprises comparing the candidateimage feature tensor with the first generative model.
 19. The method ofclaim 18, further comprising: applying a weight to the featuresextracted from the candidate image to generate a set of weightedcandidate image features, wherein the candidate image feature tensor isgenerated based on the set of weighted candidate image features.
 20. Themethod of claim 14, wherein extracting the first set of features fromeach training image comprises extracting at least one of a set of objectfeatures, a set of scene features, a set of intensity features, a set ofcontrast features, a set of color features, and a set of blurrinessfeatures from each training image.